220 citations · 1.2k across the 49 of their papers we have counts for
9 papers · 1 filter
Negative Results of Fusing Code and Documentation for Learning to Accurately Identify Sensitive Source and Sink Methods An Application to the Android Framework for Data Leak Detection
Jordan Samhi, Maria Kober, Abdoul Kader Kabore +3
Apps on mobile phones manipulate all sorts of data, including sensitive data, leading to privacy-related concerns. Recent regulations like the European GDPR provide rules for the p…
Demystifying Hidden Sensitive Operations in Android apps
Xiaoyu Sun, Xiao Chen, Li Li +5
Security of Android devices is now paramount, given their wide adoption among consumers. As researchers develop tools for statically or dynamically detecting suspicious apps, malwa…
A two-steps approach to improve the performance of Android malware detectors
Nadia Daoudi, Kevin Allix, Tegawendé F. Bissyandé +1
The popularity of Android OS has made it an appealing target to malware developers. To evade detection, including by ML-based techniques, attackers invest in creating malware that…
TriggerZoo: A Dataset of Android Applications Automatically Infected with Logic Bombs
Jordan Samhi, Tegawendé F. Bissyandé, Jacques Klein
Many Android apps analyzers rely, among other techniques, on dynamic analysis to monitor their runtime behavior and detect potential security threats. However, malicious developers…
Difuzer: Uncovering Suspicious Hidden Sensitive Operations in Android Apps
Jordan Samhi, Li Li, Tegawendé F. Bissyandé +1
One prominent tactic used to keep malicious behavior from being detected during dynamic test campaigns is logic bombs, where malicious operations are triggered only when specific c…
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
Nadia Daoudi, Jordan Samhi, Abdoul Kader Kabore +3
Computer vision has witnessed several advances in recent years, with unprecedented performance provided by deep representation learning research. Image formats thus appear attracti…